The diagnostic methods in training science concentrate on the core areas of training, competition, and performance. The methods commonly used are characterized by a high degree of practical applicability and distinct structural complexity. These characteristics have led to the question which scientific methods fit best for resolving problems like, for example, the optimization of athletic performance, efficient planning and monitoring of training processes, effective talent screening, selection and development, or the formation of analytical models. All these questions have not yet been answered sufficiently. Aside from the traditional mathematical approaches on the basis of the linear model, nonlinear methods in the field of neural networks are used in this dissertation. These nonlinear diagnostic methods are especially suitable for the analysis of coherent patterns in time series such as training processes. In the theoretical part of the dissertation, common aspects, mutual dependencies, and differences between training, competitionThe diagnostic methods in training science concentrate on the core areas of training, competition, and performance. The methods commonly used are characterized by a high degree of practical applicability and distinct structural complexity. These characteristics have led to the question which scientific methods fit best for resolving problems like, for example, the optimization of athletic performance, efficient planning and monitoring of training processes, effective talent screening, selection and development, or the formation of analytical models. All these questions have not yet been answered sufficiently. Aside from the traditional mathematical approaches on the basis of the linear model, nonlinear methods in the field of neural networks are used in this dissertation. These nonlinear diagnostic methods are especially suitable for the analysis of coherent patterns in time series such as training processes. In the theoretical part of the dissertation, common aspects, mutual dependencies, and differences between training, competition, and performance are examined. In this context, a bridge is built between the diagnostic purposes in these fields and suitable nonlinear methods. Along the lines of the neural networks theory, the basic models Perceptron, Multilayer-Perceptron, and Self-Organizing Feature Maps are subsequently elucidated. In the empirical part of the thesis, three studies conducted with top level adolescent swimmers are presented that focus on the nonlinear analysis of personal athletic ability structures, different states of athletic shape, and the prognosis of athletic talent. The nonlinear methods are thus examined as to how worthwhile they are for analytical purposes in training science on the one hand, and they are compared to each other as well as to linear methods on the other hand.…